Kimi K2.6 (thinking) · HLE-Full(w/ tools) ↗
1 more reported result
34.7 · Kimi K2.6 (thinking) · HLE-Full · Kimi K2.6 technical model card ↗
69 benchmarks · large dot: Kimi K2.6; small dots: closest models where reported
Kimi K2.6 (thinking) · HLE-Full(w/ tools) ↗
34.7 · Kimi K2.6 (thinking) · HLE-Full · Kimi K2.6 technical model card ↗
Kimi K2.6 (thinking) · BrowseComp ↗
Kimi K2.6 (thinking) · DeepSearchQA(f1-score) ↗
83.0 · Kimi K2.6 (thinking) · DeepSearchQA(accuracy) · Kimi K2.6 technical model card ↗
Kimi K2.6 (thinking) · WideSearch (item-f1) ↗
Kimi K2.6 (thinking) · Toolathlon ↗
Kimi K2.6 (thinking) · MCPMark ↗
Kimi K2.6 (thinking) · Claw Eval (pass^3) ↗
80.9 · Kimi K2.6 (thinking) · Claw Eval (pass@3) · Kimi K2.6 technical model card ↗
Kimi K2.6 (thinking) · APEX-Agents ↗
Kimi K2.6 (thinking) · OSWorld-Verified ↗
Kimi K2.6 (thinking) · Terminal-Bench 2.0(Terminus-2) ↗
Kimi K2.6 (thinking) · SWE-Bench Pro ↗
Kimi K2.6 (thinking) · SWE-Bench Multilingual ↗
Kimi K2.6 · SWE-Bench verified · Best score (across scorers) · % ↗
76.2 % · Kimi K2.6 (Mini-SWE-agent) · SWE-bench Verified · Score · Vals AI · SWE-bench Verified ↗
80.2 · Kimi K2.6 (thinking) · SWE-Bench Verified · Kimi K2.6 technical model card ↗
Kimi K2.6 (thinking) · SciCode ↗
53.47% · Kimi K2.6 · SciCode · Score · % · Epoch AI · SciCode ↗
Kimi K2.6 (thinking) · OJBench (python) ↗
Kimi K2.6 · LiveCodeBench · Accuracy ↗
89.6 · Kimi K2.6 (thinking) · LiveCodeBench (v6) · Kimi K2.6 technical model card ↗
Kimi K2.6 (thinking) · AIME 2026 ↗
Kimi K2.6 (thinking) · HMMT 2026 (Feb) ↗
Kimi K2.6 (thinking) · IMO-AnswerBench ↗
Kimi K2.6 · GPQA diamond · Best score (across scorers) · % ↗
89.14 % · Kimi K2.6 · GPQA Diamond · Accuracy · Vals AI · GPQA Diamond ↗
90.5 · Kimi K2.6 (thinking) · GPQA-Diamond · Kimi K2.6 technical model card ↗
Kimi K2.6 · MMMU Pro · Accuracy ↗
79.4 · Kimi K2.6 (thinking) · MMMU-Pro · Kimi K2.6 technical model card ↗
80.1 · Kimi K2.6 (thinking) · MMMU-Pro (w/ python) · Kimi K2.6 technical model card ↗
Kimi K2.6 (thinking) · CharXiv (RQ) ↗
86.7 · Kimi K2.6 (thinking) · CharXiv (RQ) (w/ python) · Kimi K2.6 technical model card ↗
Kimi K2.6 (thinking) · MathVision ↗
93.2 · Kimi K2.6 (thinking) · MathVision (w/ python) · Kimi K2.6 technical model card ↗
Kimi K2.6 (thinking) · BabyVision ↗
68.5 · Kimi K2.6 (thinking) · BabyVision (w/ python) · Kimi K2.6 technical model card ↗
Kimi K2.6 (thinking) · V* (w/ python) ↗
Kimi K2.6 · FrontierMath-2025-02-28-Private · Best score (across scorers) · % ↗
Kimi K2.6 · FrontierMath-Tier-4-2025-07-01-Private · Best score (across scorers) · % ↗
Kimi K2.6 · FrontierMath-Tiers-1-3-v2-Private · Best score (across scorers) · % ↗
Kimi K2.6 · FrontierMath-Tier-4-v2-Private · Best score (across scorers) · % ↗
Kimi K2.6 · OTIS Mock AIME 2024-2025 · Best score (across scorers) · % ↗
Kimi K2.6 · SimpleQA Verified · Best score (across scorers) · % ↗
Kimi K2.6 · Chess Puzzles · Best score (across scorers) · % ↗
Kimi K2.6 · EBR-bench · Best score (across scorers) · % ↗
Kimi K2.6 · Mystery Game Puzzles · Best score (across scorers) · % ↗
12% · Kimi K2.6 · Mystery Game Puzzles · Best score (across scorers) · % · Epoch AI · Mystery Game Puzzles ↗
Kimi K2.6 · WeirdML · Accuracy · % ↗
Kimi K2.6 · GBAEval · Overall score · % ↗
Kimi K2.6 · CritPt · Accuracy · % ↗
Kimi K2.6 · ExploitBench · Mean capability · % ↗
16.3% · Kimi K2.6 · ExploitBench · Mean capability · % · Epoch AI · ExploitBench ↗
Kimi K2.6 · OSWorld 2.0 · Binary accuracy · % ↗
Kimi K2.6 · ProofBench · Accuracy · % ↗
Kimi K2.6 · LMCA · Score · % ↗
Kimi K2.6 · DTBench · Accuracy · % ↗
Kimi K2.6 · Furniture Assembly · Best score (across scorers) · % ↗
Kimi K2.6 · GDP.pdf · GDP.pdf score · % ↗
Kimi K2.6 · WebDev Arena · Elo ↗
Kimi K2.6 · Blueprint-Bench 2 · Score · % ↗
Kimi K2.6 · General ECI · 26 October 2026 fit ↗
Kimi K2.6 · Vals Multimodal Index · Accuracy ↗
Kimi K2.6 · Legal Research Bench · All-Pass ↗
Kimi K2.6 · LegalBench · Accuracy ↗
Kimi K2.6 · Harvey's Legal Agent Benchmark v1 · Accuracy ↗
Kimi K2.6 · CaseLaw · Accuracy ↗
Kimi K2.6 · Tax Agent Bench · Accuracy ↗
Kimi K2.6 · Finance Agent v2 · Accuracy ↗
Kimi K2.6 · Excel Modeling Benchmark v1 · Accuracy ↗
Kimi K2.6 · CorpFin · Accuracy ↗
Kimi K2.6 · MortgageTax · Accuracy ↗
74.65 % · Kimi K2.6 · TaxEval · Accuracy · Vals AI · TaxEval ↗
Kimi K2.6 · MedCode · Accuracy ↗
Kimi K2.6 · MedScribe · Accuracy ↗
Kimi K2.6 · MMLU Pro · Accuracy ↗
Kimi K2.6 · SAGE · Accuracy ↗
Kimi K2.6 (OpenHands) · Vibe Code Bench · original task set ↗
Kimi K2.6 · ProgramBench · Accuracy ↗
Kimi K2.6 · Code Migration v1 · Accuracy ↗
Kimi K2.6 · Public Benefits Bench · Accuracy ↗
53.38 % · Kimi K2.6 · Public Benefits Bench v1 · Accuracy · Vals AI · Public Benefits Bench v1 ↗
kimi-k2.6 · DistressBench · Score ↗
Kimi k2.6 · HiL-Bench (Human-in-Loop Benchmark) · Score ↗
kimi-k2.6 · Average contest performance ↗
Kimi K2.6 · End-of-year balance ↗
Similar positions on shared benchmark leaderboards. Smaller gaps are closer.
22 of 33 shared benchmark comparisons
29 of 33 shared benchmark comparisons
25 of 33 shared benchmark comparisons
Mean absolute gap in percentile positions on shared benchmark settings; lower is closer. At least 60% overlap is required, with a minimum of three when available. Independent evaluations are used when there are at least three; otherwise published report comparisons are used. Repeated model observations within one setting are averaged before ranking. Writing-style traits and composite indices are excluded. This describes similarity, not an overall quality ranking.
Earlier-2026 baseline cohort from the official model card. Asterisks identify competitor scores re-evaluated by Kimi rather than cited from their reports. Coding scores average ten runs; vision averages three runs. Tool-enabled and no-tools results, F1 and exact accuracy, pass@3 and pass^3 remain separate rows. MCPMark here predates the Verified default. APEX uses 452 public tasks. Reviewed 2026-10-26.
Read the official model report ↗Scientist-authored coding problems require translating domain knowledge into numerical implementations.
Expert-authored questions stretch academic reasoning and knowledge across many disciplines.
A demanding subset of expert-written science questions tests detailed reasoning in specialist domains.
Academic questions require visual understanding as well as language reasoning, with measures against text-only shortcuts.
Date-bounded contest problems test coding and related code-repair or execution skills.
Human-reviewed repository issues test whether an agent can implement a repair in an existing Python codebase.
Long-horizon software-engineering tasks extend repository evaluation beyond small isolated edits.
Language-model agents act in real computer environments across applications, using reproducible setup and evaluation scripts.
Difficult information-seeking questions test a browsing agent’s ability to locate and combine evidence.
Visual problem solving tests whether a model can reason over image structure rather than rely only on familiar textual knowledge.
Web research tasks require collecting a complete set of answers rather than finding a single plausible fact.
Broad information gathering asks an agent to identify many entities and their attributes across web sources.
Long-horizon tasks require coordinating tools and completing a practical workflow.
Agents work through MCP-connected application and filesystem tasks with programmatic verification.
Tool-based agent evaluations report both repeatability and success across multiple attempts.
An agent completes technical work in a terminal under executable task checks.
Repository-level software repair broadens evaluation across programming languages.
Programming problems are solved against an online-judge-style evaluation, including a named Python setting in model reports.
A new annual contest set tests mathematical reasoning through short integer answers.
The February Harvard–MIT Mathematics Tournament provides a dated contest-math evaluation.
Olympiad-related mathematics is evaluated through answer-oriented scoring in frontier model reports.
Scientific charts require models to read visual information and reason over what the figure actually shows.
Mathematical problems with visual context require reasoning over both the statement and the supplied figures.
Detailed visual questions test the ability to locate and interpret relevant evidence inside an image.
Professional-service workflows require sustained work across files and tools.
COMPARISON CHECKLIST
Match benchmark version, task subset, model effort, harness, tool access, attempt budget, scoring rule, and fallback behavior. A shared benchmark name is the start of a comparison, not proof that two reported scores use the same contract.
Kimi original report ↗